Military Eligibility for the Supplemental Nutrition Assistance Program: Simulating the exemption of the basic allowance for housing from gross income
Abstract: Twenty-four percent of active-duty service member households experienced food insecurity in 2020; however, limited data have suggested that few participate in the Supplemental Nutrition Assistance Program (SNAP). A potential reason for low SNAP participation among active-duty military households is that the basic allowance for housing (BAH) is considered countable income for SNAP eligibility date. This study explores how many more service members’ households, referred to as “SNAP units” (that is, a group of individuals who live together and regularly buy food and prepare meals together), would become eligible for SNAP benefits if the BAH is excluded from countable income in deciding eligibility. This study used 2016–2020 American Community Survey 5-y estimates to construct a sample of active-duty military households combined with data on military pay and allowances to simulate changes to SNAP eligibility and poverty status with a BAH exemption as well as impacts on federal spending on SNAP. Eligibility for SNAP among military SNAP units increases from 0.4% to 1.5% (263% increase) if a service member’s BAH was exempted from their gross income. The increase was driven by SNAP units whose highest-ranking service member was from the noncommissioned officer ranks without dependents. As more military SNAP units became eligible and chose to participate, annual SNAP disbursements (that is, amount of funds spent on SNAP) for the whole program increased by up to 1.3%, compared with FY16–20 SNAP disbursements. With an increase in SNAP participation, the poverty rate among military SNAP units decreases from 8.7% to 1.4% (83.9% decrease). Exempting service members’ BAH from their gross income would likely increase SNAP eligibility and participation among military households and, in turn, reduce poverty.
Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.